My Privacy My Decision: Control Of Photo Sharing On Online Social Networks

ABSTRACT:

Photo sharing is an attractive feature which popularizes Online Social Networks (OSNs). Unfortunately, it may leak users’ privacy if they are allowed to post, comment, and tag a photo freely. In this paper, we attempt to address this issue and study the scenario when a user shares a photo containing individuals other than himself/herself (termed co-photo for short). To prevent possible privacy leakage of a photo, we design a mechanism to enable each individual in a photo be aware of the posting activity and participate in the decision making on the photo posting. For this purpose, we need an efficient facial recognition (FR) system that can recognize everyone in the photo. However, more demanding privacy setting may limit the number of the photos publicly available to train the FR system. To deal with this dilemma, our mechanism attempts to utilize users’ private photos to design a personalized FR system specifically trained to differentiate possible photo co-owners without leaking their privacy. We also develop a distributed consensus based method to reduce the computational complexity and protect the private training set. We show that our system is superior to other possible approaches in terms of recognition ratio and efficiency. Our mechanism is implemented as a proof of concept Android application on Facebook’s platform.

AIM

Our mechanism attempts to utilize user`s private photos to design a personalized FR system specifically trained to differentiate possible photo co-owners without leaking their privacy.

SCOPE

 The Scope of this project is to reduce the computational complexity and protect the private training set.

EXISTING SYSTEM

For instance, nowadays we can share any photo as we like on OSNs, regardless of whether this photo contains other people (is a co-photo) or not. Currently there is no restriction with sharing of co-photos, on the contrary, social network service providers like Face book we need to elaborate on the privacy issues over OSNs. Traditionally, privacy is regarded as a state of social withdrawal. According to Altman’s privacy regulation theory, privacy is a dialectic and dynamic boundary regulation process where privacy is not static but “a selective control of access to the self or to ones group”. In this theory, “dialectic” refers to the openness and closeness of self to others and “dynamic” means the desired privacy level changes with time according to environment.

DISADVANTAGES:

1. It  may leak users’ privacy if they are allowed to post, comment, and tag a photo freely
2. Photo sharing and tagging are added, the situation becomes more complicated.

PROPOSED SYSTEM

In this paper, we propose a novel consensus based approach to achieve efficiency and privacy at the same time. The idea is to let each user only deal with his/her private photo set as the local train data and use it to learn out the local training result. After this, local training results are exchanged among users to form a global knowledge. In the next round, each user learns over his/hers local data again by taking the global knowledge as a reference. Finally the information will be spread over users and consensus could be reached. We show later that by performing local learning in parallel, efficiency and privacy could be achieved at the same time.

ADVANTAGES

1.  Designed a privacy-preserving FR system to identify individuals in a co-photo.
2. our proposed scheme be very useful in protecting users’ privacy in photo/image sharing over online social networks

SYSTEM ARCHITECTURE:

SYSTEM CONFIGURATION

HARDWARE REQUIREMENTS:-

·                Processor          -   Pentium –III

·                Speed                -    1.1 Ghz

·                RAM                 -    256 MB(min)

·                Hard Disk         -   20 GB

·                Floppy Drive    -    1.44 MB

·                Key Board                 -    Standard Windows Keyboard

·                Mouse               -    Two or Three Button Mouse

·                Monitor             -    SVGA

SOFTWARE REQUIREMENTS:-

·                Operating System              : Windows  7                                       

·                Front End                  : JSP AND SERVLET

·                Database                  : MYSQL

·                Tool                           :NETBEANS

REFERENCE:

Xu, K,Guo, Y,  Guo, L.  Fang, Y. “My Privacy My Decision: Control Of Photo Sharing On Online Social Networks”, IEEE Transactions on Dependable and Secure Computing, Volume PP,  Issue 99, JUNE  2015.